A visual energy performance assessment and decision support tool for dwellings

Amit Mhalas 0 Mohamad Kassem 0 Tracey Crosbie 0 Nashwan Dawood 0 0 Technology Futures Institute, Teesside University , Middlesbrough TS1 3BA, UK Background: The target for carbon dioxide (CO2) emissions reduction in the UK is set at 20% by 2020 and 80% by 2050. The UK housing stock is one of the least energy efficient in Europe. The energy used in homes accounts for more than a quarter of energy use and carbon dioxide emissions in Great Britain. Therefore, it is imperative to improve the energy performance of the existing housing stock and fully exploit energy efficiency and renewable energy interventions. The UK has developed several policies and initiatives to improve the energy performance of the housing stock and there are a number of databases that hold information about the condition of the housing stock. However, existing approaches and tools do not allow decision makers to assess the environmental and economic effectiveness of CO2 reduction strategies at the neighbourhood level. Methods: This research presents a methodology that integrates these energy databases with visualisation systems and multi-criteria decision analyses to enable the evaluation of the environmental and financial implications of various energy efficiency and renewable energy interventions at both building and neighbourhood levels. The methodology is prototyped in a proof-of-concept tool which is validated and tested in an empirical case study with local authorities and social housing providers. Results: The validation study compared the energy performance of the dwellings estimated by the proposed methodology with the energy performance calculated from actual survey and confirmed that the results are consistent. The case study demonstrated that the methodology and the prototype can be reliably utilised to evaluate the environmental and financial implications of various energy efficiency and renewable energy interventions. Conclusion: The findings illustrate that the tool is particularly useful for town planners, local authorities and social housing providers. They can make informed decisions about the implementation of energy policies and initiatives along with energy suppliers, building engineers and architects. The tool developed in the research and presented in this paper can contribute to meeting CO2 emission reduction targets. - Introduction There is a rising interest in tackling climate change. Subsequent to the 1992 Kyoto Protocol there is a growing incentive to reduce CO2 emissions through increased use of renewable energy sources and reducing energy demand. The UKs commitment under the protocol is for a reduction in greenhouse gas emissions of 12.5% from 1990 levels by 2012. The UK government in its Climate Change Act is committed to reduce its CO2 emissions by 80% by 2050 over its 1990 baseline (H.M. Government 2008). The UK government is also committed to meet the EU target to reduce its CO2 emissions by 20% and obtain 15% of energy from renewable sources by 2020 (House of Lords 2008). Buildings contribute almost a half of all CO2 emissions in the UK. Of those emissions 17% come from approximately 26 million residential dwellings and 18% come from 2 million non-domestic buildings (All Party Urban Development Group 2008). It is expected that about 75% of the existing domestic stock will be still present in 2050 (Wright 2008). The UK housing stock is one of the oldest and the least efficient in Europe. This poor quality housing stock means space heating consumed about 66% of the total delivered energy in 2006 (Palmer and Cooper 2011). Over 30% of the dwellings in England are thought to be non-decent i.e. they are unhealthy, in disrepair, in need of modernisation or providing insufficient thermal comfort, with 80% of these failing to meet the criteria for comfort (Communities and Local Government 2012). The reduction of CO2 emissions from the existing built environment is likely to be a key component of meeting the overall 80% CO2 emissions reduction target (Jones et al. 2007). A range of improvements through energy efficiency and renewable energy measures is promoted through Government policies and initiatives including Carbon Emissions Reduction Targets (CERT), Community Energy Savings Programme (CESP), Energy Company Obligation (ECO) and the Green Deal (DECC 2009). These initiatives include grants and advice programmes to achieve short and long term emission goals. These initiatives aim to reduce energy consumption, improve living standards and eliminate fuel poverty (DECC 2011a, b). The local development framework requires local governments to involve local community, utility providers, environmental groups and housing corporations amongst others in their appraisal and management process of the framework (Office of the Deputy Prime Minister 2010). Therefore, energy and carbon models which can undertake predictions and evaluate the potential of different energy efficiency and renewable energy interventions for the housing stock are essential for implementation of these policies and initiatives (Cheng and Steemers 2011). This paper presents a methodology and a proof-of-concept tool that together integrates energy databases with visualisation systems and multi-criteria decision analyses to enable the evaluation of the environmental and financial implications of various energy efficiency and renewable energy interventions at both building and neighbourhood levels. The proof-of-concept tool is based on a GIS platform and makes use of aerial and terrestrial imagery, digital maps and information from various national statistics and databases. First, the paper presents the gaps identified through literature review of the existing dwelling models. Second, the paper illustrates the methodology and tool developed and their validation in an empirical case study with the involvement of a local authority and a social housing provider. Finally, the discussion of the case study results is presented conducted by comparing the tool outputs with the actual energy performance data from the housing provider and estimating the potential of energy saving and CO2 emission reduction. Background The techniques to model energy consumption in the residential sector can be broadly classified into top-down and bottom-up approaches (Tuladhar et al. 2009). The approaches have a vast diversity in terms of their level of detail, their complexity, the data input required by the user, the time periods covered and their geographical coverage (Hourcade et al. 2006). Top down approaches The top-down approaches work on a macro-economic scale to model energy supply and energy demand. The development and use of these approaches grew significantly during the energy crisis in the late 1970s. The models require few details of the consumption process and treat dwellings as an energy sink and regress or apply factors that affect consumption to determine the trends (Swan and Ugursal 2009). This approach aims at fitti (...truncated)